{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Simple tutorial following the TensorFlow example of a Convolutional Network.\\n\\nParag K. Mital, Jan. 2016'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"Simple tutorial following the TensorFlow example of a Convolutional Network.\n",
    "\n",
    "Parag K. Mital, Jan. 2016\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# %% Imports\n",
    "%matplotlib inline\n",
    "import tensorflow as tf\n",
    "import tensorflow.examples.tutorials.mnist.input_data as input_data\n",
    "from libs.utils import *\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "# %% Setup input to the network and true output label.  These are\n",
    "# simply placeholders which we'll fill in later.\n",
    "mnist = input_data.read_data_sets('MNIST_data/', one_hot=True)\n",
    "x = tf.placeholder(tf.float32, [None, 784])\n",
    "y = tf.placeholder(tf.float32, [None, 10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# %% Since x is currently [batch, height*width], we need to reshape to a\n",
    "# 4-D tensor to use it in a convolutional graph.  If one component of\n",
    "# `shape` is the special value -1, the size of that dimension is\n",
    "# computed so that the total size remains constant.  Since we haven't\n",
    "# defined the batch dimension's shape yet, we use -1 to denote this\n",
    "# dimension should not change size.\n",
    "x_tensor = tf.reshape(x, [-1, 28, 28, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# %% We'll setup the first convolutional layer\n",
    "# Weight matrix is [height x width x input_channels x output_channels]\n",
    "filter_size = 5\n",
    "n_filters_1 = 16\n",
    "W_conv1 = weight_variable([filter_size, filter_size, 1, n_filters_1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# %% Bias is [output_channels]\n",
    "b_conv1 = bias_variable([n_filters_1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# %% Now we can build a graph which does the first layer of convolution:\n",
    "# we define our stride as batch x height x width x channels\n",
    "# instead of pooling, we use strides of 2 and more layers\n",
    "# with smaller filters.\n",
    "h_conv1 = tf.nn.relu(\n",
    "    tf.nn.conv2d(input=x_tensor,\n",
    "                 filter=W_conv1,\n",
    "                 strides=[1, 2, 2, 1],\n",
    "                 padding='SAME') +\n",
    "    b_conv1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# %% And just like the first layer, add additional layers to create\n",
    "# a deep net\n",
    "n_filters_2 = 16\n",
    "W_conv2 = weight_variable([filter_size, filter_size, n_filters_1, n_filters_2])\n",
    "b_conv2 = bias_variable([n_filters_2])\n",
    "h_conv2 = tf.nn.relu(\n",
    "    tf.nn.conv2d(input=h_conv1,\n",
    "                 filter=W_conv2,\n",
    "                 strides=[1, 2, 2, 1],\n",
    "                 padding='SAME') +\n",
    "    b_conv2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# %% We'll now reshape so we can connect to a fully-connected layer:\n",
    "h_conv2_flat = tf.reshape(h_conv2, [-1, 7 * 7 * n_filters_2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# %% Create a fully-connected layer:\n",
    "n_fc = 1024\n",
    "W_fc1 = weight_variable([7 * 7 * n_filters_2, n_fc])\n",
    "b_fc1 = bias_variable([n_fc])\n",
    "h_fc1 = tf.nn.relu(tf.matmul(h_conv2_flat, W_fc1) + b_fc1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# %% We can add dropout for regularizing and to reduce overfitting like so:\n",
    "keep_prob = tf.placeholder(tf.float32)\n",
    "h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# %% And finally our softmax layer:\n",
    "W_fc2 = weight_variable([n_fc, 10])\n",
    "b_fc2 = bias_variable([10])\n",
    "y_pred = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# %% Define loss/eval/training functions\n",
    "cross_entropy = -tf.reduce_sum(y * tf.log(y_pred))\n",
    "optimizer = tf.train.AdamOptimizer().minimize(cross_entropy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# %% Monitor accuracy\n",
    "correct_prediction = tf.equal(tf.argmax(y_pred, 1), tf.argmax(y, 1))\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_prediction, 'float'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# %% We now create a new session to actually perform the initialization the\n",
    "# variables:\n",
    "sess = tf.Session()\n",
    "sess.run(tf.initialize_all_variables())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9624\n",
      "0.9802\n",
      "0.9828\n",
      "0.9866\n",
      "0.987\n"
     ]
    }
   ],
   "source": [
    "# %% We'll train in minibatches and report accuracy:\n",
    "batch_size = 100\n",
    "n_epochs = 5\n",
    "for epoch_i in range(n_epochs):\n",
    "    for batch_i in range(mnist.train.num_examples // batch_size):\n",
    "        batch_xs, batch_ys = mnist.train.next_batch(batch_size)\n",
    "        sess.run(optimizer, feed_dict={\n",
    "            x: batch_xs, y: batch_ys, keep_prob: 0.5})\n",
    "    print(sess.run(accuracy,\n",
    "                   feed_dict={\n",
    "                       x: mnist.validation.images,\n",
    "                       y: mnist.validation.labels,\n",
    "                       keep_prob: 1.0\n",
    "                   }))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f04141f3390>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AmipY4AWzYfkLCjSRJV5SES9D12Qxq/14BZ/fg8dH4LRdx0fo+RFzfISOR9ou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\nqXL4fu4m2VB1R5NYUqugNXjp3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DG1KnXKakOuFSlvSJqsMS0nkGZE8SSWnkIqJLO4U8dT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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f042c16a150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %% Let's take a look at the kernels we've learned\n",
    "W = sess.run(W_conv1)\n",
    "plt.imshow(montage(W / np.max(W)), cmap='coolwarm')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
